code: def get_supported_operator_for_quantization_config(
      cls, quantization_config: QuantizationConfig
  ) -> List[OperatorPatternType]:
      for config, ops in cls.get_supported_operators():
          # note: this assumes each entry in cls.supported_spec_and_operators
          # corresponds to one spec, e.g. we don't have
          # [(spec1, op_list1), (spec1, op_list2), (spec2, op_list3)]
          # where the first and second entry have the same spec but did not
          # merge the op list
          if config == quantization_config:
              return ops
      return []
  
file_path: pytorch/torch/ao/quantization/quantizer/embedding_quantizer.py
imports: from __future__ import annotations
  import copy
  from typing import List, Set
  import torch
  import torch.nn.functional
  from torch.ao.quantization.observer import PerChannelMinMaxObserver
  from torch.ao.quantization.quantizer.quantizer import QuantizationAnnotation, QuantizationSpec, Quantizer
  from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import OperatorConfig, OperatorPatternType, QuantizationConfig
label_desc: Use image node_id 3 for calling the EmbeddingQuantizer obj's underlying member method code with example usage: obj.get_supported_operator_for_quantization_config(cls, quantization_config) and returns: list, ops